1. Field of the Invention
The present invention relates to a pattern recognition using a so-called fuzzy logic, and more particularly to a method of recognizing patterns which are not uniformly defined such as handwritten characters, by evaluating such characters with reference patterns having fuzzy contours and an apparatus for implementing the method.
2. Description of the Prior Art
Many pattern recognition techniques are known in the art which use image processing but which are, however, generally intended to recognize a uniformly defined pattern such as a typeface and are not capable of recognizing handwritten characters which are not strictly defined. For recognizing such handwritten characters, there are methods using artificial intelligence, wherein a neural network is formed by initially learning characteristics of patterns of respective characters to be recognized. Patterns or characters, desired to be recognized, are inputted thereto for recognition, characteristics of the patterns are extracted and stored and compared with subsequently input patterns to achieve recognition of the identity of the patterns.
The recognition method using a neural network, useful in recognizing handwritten characters as mentioned above, however, requires learning which must be redone every time a pattern to be recognized is newly added to the existing neural network. Specifically, even with a slight change in a single pattern to be recognized, the neural network, in many cases, must learn again all of the previously learned patterns (training data). Further, the user is not aware how the recognition result is derived by the neural network, which results in difficulties in maintenance of the neural network.
In recent years, fuzzy logic has been applied to neural networks and pattern recognition, in particular, where a pattern to be recognized is checked with each of a plurality of stored data related to characteristics of patterns possessed by a system.
Conventional recognition methods using fuzzy logic, however, require a procedure of acquiring an immense amount of knowledge related to characteristics of respective objective patterns, referred to as a rule, as well as an algorithm for extracting characteristics of respective patterns for building a system including software, whereby many rules and membership functions for fuzzy sets are required. This results in complicated processing, long learning times and much computing overhead during the recognition process.